CRLB analysis of wireless cognitive location with different short-range measurements

Because of the wide application and great market potential of location-aware services, the research of wireless location techniques for the fourth generation （4G） mobile communications is being paid more attention. Wireless cognitive location （WCL） techniques for next generation wireless networks have been proposed in recent years. This article investigates the changes of the positioning accuracy of WCL algorithm when different methods are adopted to measure the short-range （SR） information. By fJrst completing Cramer-Rao lower bound （CRLB） analysis of the WCL algorithm with SR measurements based on time of arrival （TOA） and received signal strength （RSS）, it is discovered that TOA-based or time difference of arrival （TDOA） -based SR measurement can make WCL algorithms achieve higher accuracy than RSS mode, which is also verified by numerical simulation in the article. The conclusions can instruct the design of novel WCL-based location algorithms.     

孤子能量增强对色散管理孤子系统性能的影响

本文采用数值系统仿真的方法,通过比较孤子功率增强输入和孤子基态功率输入时,孤子脉冲的传输波形,分析孤子功率增强对色散管理孤子系统传输性能的影响。结果表明,在色散管理孤子系统中,采用功率增强输入可以非常好的提高长距离系统传输性能。     

Accurate segmentation of land regions in historical cadastral maps

Historical cadastral maps are valuable sources for historians to study social and economic background of changes in land uses or ownerships. In order to conduct large-scale historical research, it is essential to digitize the cadastral maps. As being established in antiquity, however, they suffer from significant noise artifacts attributed to hand-drawn cartography. In this paper, we propose a novel method of extracting land regions automatically in historical cadastral maps. First, we remove grid reference lines based on the density of the black pixel with the help of the jittering. Then, we remove land owner labels by considering morphological and geometrical characteristics of thinned image. We subsequently reconstruct land boundaries. Finally, the land regions of a user’s interest are modeled by their polygonal approximations. Our segmentation results were compared with manually segmented results and showed that the proposed method extracted the land regions accurately for assisting cadastral mapping in historical research.     

An efficient reversible data hiding scheme using prediction and optimal side information selection

In this paper, we present an efficient histogram shifting (HS) based reversible data hiding scheme for copyright protection of multimedia. Firstly, an improved HS based multi-layer embedding process for rhombus prediction is employed by introducing a control parameter to explore the correlation of prediction errors. A rate-distortion model for HS embedding is then developed for optimal side information selection, which is especially suitable for low payload reversible data hiding when only a single layer embedding is required. Finally, a modified location map is constructed to facilitate the compression of location map and further increase the embedding capacity. Compared with similar schemes, experimental results demonstrate the superior performance of the proposed scheme in the terms of embedding capacity and stego-image quality.     

An extended context-based entropy hybrid modeling for image compression

Recently deep learning has been introduced to the field of image compression. In this paper, we present a hybrid coding framework that combines entropy coding, deep learning, and traditional coding framework. In the base layer of the encoding, we use convolutional neural networks to learn the latent representation and importance map of the original image respectively. The importance map is then used to guide the bit allocation of the latent representation. A context model is also developed to help the entropy coding after the masked quantization. Another network is used to get a coarse reconstruction of the image in the base layer. The residual between the input and the coarse reconstruction is then obtained and encoded by the traditional BPG codec as the enhancement layer of the bit stream. We only need to train a basic model and the proposed scheme can realize image compression at different bit rates, thanks to the use of the traditional codec. Experimental results using the Kodak, Urban100 and BSD100 datasets show that the proposed scheme outperforms many deep learning-based methods and traditional codecs including BPG in MS-SSIM metric across a wide range of bit rates. It also exceeds some latest hybrid schemes in RGB444 domain on Kodak dataset in both PSNR and MS-SSIM metrics.     

Robust depth map inpainting using superpixels and non-local Gauss–Markov random field prior

Depth maps contain 3D geometrical information of a scene which can be useful in many applications. In this work we address the task of inpainting a depth map afflicted by regions with missing data. Initially, we inpaint depth values missing at random locations as well as due to overlaid text. Subsequently, we propose an approach for filling large holes in the input depth map wherein superpixel division of the corresponding RGB image is also exploited. We use a non-local extension of the classical Gauss–Markov random field model for the completed depth map, so that missing information in the degraded observation can be estimated depending upon self-similarities between non-local patches inside a superpixel based search window. Several experiments performed with disparity maps and real world depth data exhibit the efficacy of the proposed algorithm.     

基于Shear变换的彩色地形图混沌加密算法

针对目前研究地形图图像加密算法较少,以及现有图像加密算法易被破解的问题,提出基于Shear变换的彩色地形图混沌加密算法。该算法充分利用了Shear变换的多方向性,能够很好地对地形图的线划要素信息进行置乱的特点,以及Lorenz混沌系统具有极强初值敏感性和更大的密匙空间和安全性的特点。通过对彩色地形图的加密和解密实验,以及抗攻击性能对比实验,表明使用该算法加密后的彩色地形图具有更高的安全性,并且载体图像具有较高的抗攻击性能。     

一种新型离散指数混沌映射的实现及其研究

针对Logistic映射参数简单、混沌区间范围小的缺点,提出一种新型的离散指数混沌映射。通过引入指数项和增加参数个数,进而扩大映射的混沌区间。利用常规的动力学分析方法,对指数混沌映射进行了动力学特性分析,包括Lyapunov指数图、分岔图等。在此基础上,对Logistic映射和离散指数混沌映射进行了特性比较。最后对由该离散混沌映射产生的序列进行NIST测试,测试结果表明序列性能更良好。     

认知无线网络中小型移动主用户的感知与定位研究

主要对认知无线网络中小型移动主用户的感知与定位技术进行了研究。首先简要介绍认知无线电的概念和研究现状,并对大型主用户和小型主用户的特征进行了对比、归纳及总结;然后描述了小型移动主用户难以感知的特点,并对小型主用户感知与定位的技术进行分类,重点分析和探讨了有关运用不同频谱感知技术来实现定位方法的优缺点;最后给出了认知无线网络中感知小型移动主用户的重点研究方向的建议。     

卫星认知无线通信中频谱感知算法比较

以实现卫星认知无线通信中频谱空穴探测为目的,列举并比较了目前用于频谱感知的六种算法。根据各个算法的自身特点指出其应用场合,分别分析了它们的优缺点,通过计算机仿真给出了能量检测、匹配滤波和压缩感知的检测性能,并指出压缩感知对硬件复杂度的要求最小,适合卫星认知无线通信。